hassanRagab/Qwen2.5-1.5B-Reasoning-Hybrid-SFT
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 13, 2026Architecture:Transformer Featherless Exclusive Cold
hassanRagab/Qwen2.5-1.5B-Reasoning-Hybrid-SFT is a 1.5 billion parameter language model based on the Qwen2.5 architecture. This model is fine-tuned for reasoning tasks, incorporating a hybrid approach to enhance its logical capabilities. It is designed for applications requiring improved analytical and problem-solving skills within a compact parameter count, making it suitable for efficient deployment.
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Overview
This model, hassanRagab/Qwen2.5-1.5B-Reasoning-Hybrid-SFT, is a 1.5 billion parameter language model built upon the Qwen2.5 architecture. It has been specifically fine-tuned using a hybrid approach to bolster its reasoning capabilities, aiming to provide enhanced logical processing within a smaller model footprint.
Key Capabilities
- Reasoning Enhancement: The model is designed with a focus on improving reasoning skills through its specialized fine-tuning.
- Compact Size: With 1.5 billion parameters, it offers a relatively efficient solution for deployment compared to larger models.
- Qwen2.5 Base: Leverages the foundational strengths of the Qwen2.5 architecture.
Good For
- Applications requiring improved analytical and problem-solving abilities.
- Scenarios where computational resources are a consideration, benefiting from its smaller parameter count.
- Tasks that can leverage a model with enhanced logical processing, such as question answering or structured data interpretation, where reasoning is critical.